On-line fuzzy neural modeling with structure and parameters updating

Abstract

In this paper we propose a novel online clustering approach which can be applied in a general class of fuzzy neural networks. Both structure identification and parameters learning are online. The new clustering method for the structure identification can separate input-output data into different groups (rulenumber) by online input/output data. For the parameter learning, our algorithm has two advantages over the others. First, the normal methods for parameter identification are based on a fixed structure and whole data, for example ANFIS, but after clustering we know each group corresponds to one rule, so we train each rule by its group data, it is more effective. Second, we give a time-varying learning rate for the common used backpropagation algorithm, we prove that the new algorithm is stable and faster than backpropagation algorithm.